Estimating the hazard of tree fall along railway lines: a new GIS tool

Sonja Szymczak (), Frederick Bott, Pierre Babeck, Annett Frick, Benjamin Stöckigt and Kathrin Wagner
Additional contact information
Sonja Szymczak: Federal Railway Authority
Frederick Bott: Federal Railway Authority
Pierre Babeck: LUP – Luftbild Umwelt Planung GmbH
Annett Frick: LUP – Luftbild Umwelt Planung GmbH
Benjamin Stöckigt: LUP – Luftbild Umwelt Planung GmbH
Kathrin Wagner: LUP – Luftbild Umwelt Planung GmbH

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 112, issue 3, No 16, 2237-2258

Abstract: Abstract Trees along railway networks represent a high risk due to their potential to fall during extreme weather events. The identification of locations along railway tracks with highest tree fall hazard is an important part of a proactive natural hazard management. A new user-friendly GIS tool (as ArcGIS toolbox) was developed that provides the opportunity to detect individual trees along railway lines and to estimate the hazard of tree fall. By an automated analysis of open source digital remote sensing data and additional open source geodata, the tool allows for an up-to-date and area-wide monitoring of trees on railway lines and other infrastructural elements. Important parameters describing meteorological conditions, site conditions, topographic conditions and tree characteristics are implemented. The tool was successfully tested and applied to two federal states in Germany (Northrhine-Westphalia and Thuringia). Due to the automatization of most of the processes, it is possible to extend the application to larger areas with low effort, i.e., to the Germany-wide rail network or to other countries. It is also possible to perform the analysis for other modes of transport. In the context of natural hazard management, the tool can be applied in prevention and can usefully support already existing vegetation management concepts.

Keywords: Tree fall hazard; Exposure analysis; Vegetation monitoring; Remote sensing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05263-5

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